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Analysis

1 / 2,649
1.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

2.3786855AUTOMATED DATA PROCESSING AND MACHINE LEARNING MODEL GENERATION
EP - 03.03.2021
Int.Class G06N 5/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
 Appl.No 19290076 Applicant ACCENTURE GLOBAL SOLUTIONS LTD Inventor HIGGINS LUKE
A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.
3.2632656Autonomous vehicles
GB - 19.02.2025
Int.Class B60W 60/00
BPERFORMING OPERATIONS; TRANSPORTING
60VEHICLES IN GENERAL
WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
60Drive control systems specially adapted for autonomous road vehicles
 Appl.No 202312408 Applicant WAYVE TECH LTD Inventor LONG CHEN
An autonomous vehicle comprises a sensor unit 102 configured to generate an environmental signal corresponding to a surrounding environment. A vector generator unit 108 generates a numeric vector from the environmental signal received from the sensor unit. A pre- processing unit receives the numeric vector and generates a vector embedding from that numeric vector. The vector embedding comprises a structured semantic representation of the surrounding environment of the autonomous vehicle 110. A language model unit uses a machine learning language model 112 to generate a textual data output based on the vector embedding. A controller 114 receives the textual data output and generates one or more driving commands, and the controller operates the autonomous vehicle 130 based on those commands.
4.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
5.WO/2018/081751VIDEO TAGGING SYSTEM AND METHOD
WO - 03.05.2018
Int.Class G06E 1/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
EOPTICAL COMPUTING DEVICES
1Devices for processing exclusively digital data
 Appl.No PCT/US2017/059119 Applicant VILYNX, INC. Inventor BOU BALUST, Elisenda
An automatic video tagging system which learns from videos, their web context and comments shared on social networks is described. Massive multimedia collections are analyzed by Internet crawling and a knowledge base is maintained that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. Practical applications of video recognition require a label scheme that is appealing to the end-user (i.e. obtained from social curation) and a training dataset that can be updated in real-time to be able to recognize new actions, scenes and people. To create this dataset that evolves in real-time and uses labels that are relevant to the users, a weakly-supervised deep learning approach is utilized combining both a machine-learning pre-processing stage together with a set of keywords obtained from the internet. The resulting tags combined with videos and summaries of videos are used with deep learning to train a neural network in an unsupervised manner that allows the tagging system to go from an image to a set of tags for the image and then to the visual representation of a tag.
6.20190164164COLLABORATIVE PATTERN RECOGNITION SYSTEM
US - 30.05.2019
Int.Class G06Q 20/40
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
 Appl.No 16172751 Applicant Krishna Pasupathy Karambakkam Inventor Krishna Pasupathy Karambakkam

Apparatus and associated methods relate to a pattern recognition system configured to classify a transaction as anomalous or not anomalous as a function of a predictive analytic model configured to detect anomalies, generate a rule based on expert analysis of a limited number of data samples to classify as anomalous a transaction erroneously classified as not anomalous, augment the predictive analytic model with the generated rule, and deploy the augmented predictive analytic model to automatically identify an attack early in a live transaction stream. In some examples, the transaction may be a bank card purchase. Some transactions may be classified anomalous due to fraud, compliance violation such as money laundering, or terrorist funding. The predictive analytic model may be, for example, a decision tree followed by a regression model. Various embodiments may advantageously generate rules based on transaction criteria selected by human experts exploring and manipulating visually perceptible transaction representations.

7.20200231466INTELLIGENT SYSTEMS AND METHODS FOR PROCESS AND ASSET HEALTH DIAGNOSIS, ANOMOLY DETECTION AND CONTROL IN WASTEWATER TREATMENT PLANTS OR DRINKING WATER PLANTS
US - 23.07.2020
Int.Class C02F 1/00
CCHEMISTRY; METALLURGY
02TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
FTREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
1Treatment of water, waste water, or sewage
 Appl.No 16472998 Applicant Zijun XIA Inventor Su LU

Described herein are systems and methods of analyzing data acquired from a water plant, both historical and in real-time, making determinations about process and asset health diagnosis and anomaly detection using advanced techniques, and controlling the plant and/or providing alerts based on such determinations.

8.20230320642SYSTEMS AND METHODS FOR TECHNIQUES TO PROCESS, ANALYZE AND MODEL INTERACTIVE VERBAL DATA FOR MULTIPLE INDIVIDUALS
US - 12.10.2023
Int.Class A61B 5/16
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
16Devices for psychotechnics; Testing reaction times
 Appl.No 18130947 Applicant The Trustees of Columbia University in the City of New York Inventor Baihan Lin

Disclosed are methods, systems, and other implementations for processing, analyzing, and modelling psychotherapy data. The implementations include a method for analyzing psychotherapy data that includes obtaining transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting speech segments from the transcript data related to one or more of the patient or the therapist, applying a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing the weighted topic labels to derive a psychiatric assessment for the patient.

9.WO/2019/028279METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
WO - 07.02.2019
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2018/045051 Applicant VERITONE, INC. Inventor STEELBERG, Chad
A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.
10.WO/2025/036885AUTONOMOUS VEHICLES
WO - 20.02.2025
Int.Class G06F 40/30
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
40Handling natural language data
30Semantic analysis
 Appl.No PCT/EP2024/072761 Applicant WAYVE TECHNOLOGIES LTD. Inventor CHEN, Long
An autonomous vehicle comprises a sensor unit configured to generate an environmental signal corresponding to a surrounding environment. A vector generator unit generates a numeric vector from the environmental signal received from the sensor unit. A pre-processing unit receives the numeric vector and generates a vector embedding from that numeric vector. The vector embedding comprises a structured semantic representation of the surrounding environment of the autonomous vehicle. A language model unit uses a machine learning language model to generate a textual data output based on the vector embedding. A controller receives the textual data output and generates one or more driving commands, and the controller operates the autonomous vehicle based on those commands.
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